Many thanks for the prompt response.

However, I am afraid that it is not completely clear for me. I

apologize, I am not a statistician.

Sorry, may be what I will say make totally non sense, but what I

understood is the following:

Let's suppose that I need to predict cancer-specific survival using

the variables X and Y. What I need to do is to develop a model that

include these variables and predict cancer-specific survival using the

competing-risks regression. Then, I shall calculate the "predictions"

of this model at a certain time point, then I shall use these

prediction as an "endpoint", and predict it using a linear regression

model that include the same variables, i.e X and Y. Finally, I use the

coefficients of this final model to develop a nomogram. Is that

correct?

Many thanks again

Should I calculate the "prediction" of the competing-risks regression

model, and then use this "prediction" as an "endpoint" and predict it

using a linear regression by including the same variables as

predictors?

On Fri, Jun 24, 2011 at 1:27 PM, Frank Harrell [via R]

wrote:

Replace the Design package with the rms package. Â Use the ordinary linear

regression trick to predict the linear predictor from the competing risk

regression, then use nomogram on this new model (that merely represents the

fit of interest).

Frank

Firas Abdollah wrote:

Hi R users,

I'd like to draw a nomogram using a competing-risks regression (crr function

in R), rather than a cox regression. However, the nomogram function provided

in the Design package is not good for this purpose.

Do you have any suggestion.

I really appreciate your help

Many thanks

F.Abdollah, MD

San-Raffele hospital

Milan, Italy

Frank Harrell

Department of Biostatistics, Vanderbilt University

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Firas Abdollah, MD

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San Raffaele Hospital

Vita-Salute University,

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Fax. +39 02 2643 7298

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